GPP Stacking with Park Factors

GPP Stacking with Park Factors

Good morning folks. Welcome to the ninth week of GPP Stacking with Park Factors. For the last eight weeks I’ve been hounding you guys about the differences between stacking the best and worst parks for offensive in GPPs at DraftKings. During my journey I’ve discussed ownership percentages, the importance of pitching, making sound selections and a bunch of other important MLB DFS concepts.

If you’re just catching up for the first time, I ask you to take a deep dive into the previous eight weeks of work. You can find the other pieces by selecting “Park Factors” from the MLB Free dropdown menu. If you’re too lazy to look back, let me explain the experiment to you. Each week I stack lineups from different games using park factors and enter them into GPPs at DraftKings. A park factor is simply a relative rating of a ballparks aptitude for offense.

Since the second week of the season, I’ve been using a manipulation of the FanGraphs park factors by handedness as shown below:

This week was one of those really easy weeks in terms of gameplay and lineup construction thanks to the games in Coors Field.

However, I was particularly interested in discussing the game that took place at Coors Field last night and applying some of the concepts we’ve discussed previously to it.

First you might notice that I’ve only included the screenshot to the “best park” lineup. That’s because I solely want to focus on this lineup and the way this GPP played out. If you look closely, I’ve selected the “show only mine” button on the left hand side of the standings, and you can see that my “worst park” lineup wasn’t too bad, but not good enough for cash.

The really interesting part of this lineup I created was the ownership percentages. If you played last night, I’m sure you at one point were very concerned about the weather at Coors Field. There were severe thunderstorm and tornado warnings prior to the early lock, and it was wreaking havoc on Twitter and through the DFS-sphere.

However, the weather at Coors didn’t really seem to crush the ownership percentages as much as I thought it would. I’m sure the levels for “easy picks” Joc Pederson and Adrian Gonzalez would have been higher, but I’m actually surprised that they were 14.9% and 28.2% respectively. Still, any reduction in ownership percentages on these players increased my EV in the event the game got played as it did last night.

I intentionally left my best park stack like this for a few reasons. 1) I thought that if the game did go, I might have a huge advantage on a lot of people who swapped off. 2) I was purely interested in seeing the ownership percentages for the purpose of this experiment.

I think this is the first time I’ve really run into this circumstance while doing this experiment. There have been weather concerns before, and even postponements at Coors Field, but I knew well ahead of time to get those guys out. The danger of not swapping these players out in a tournament, especially last night, was that there were not many teams to swap to. If you had the guts to leave all of your Coors Field players (and had not already done so before initial roster lock), you were busy selecting between the Mets, Padres, Angels and Rays to fill out your roster.

You might be scratching your head at the point I’m trying to get across, but I felt it was necessary to show some effects of external factors in MLB DFS that we don’t typically see in other sports. Games with weather concerns in good hitter’s parks allow an opportunity to combine both the reasons we stack good hitter’s parks (the inherent offensive upside) and bad hitter’s parks (low ownerships or at least lower than they should be).

As for my team, it was pretty solid. Joc Pederson hit another homerun, my pitching staff was solid enough, and the rest of the team filled in nicely. Notice once again, no zero from any player on my team! Hooray!

I’ve updated the table below, updating the ROI and score totals for the week and the season to date.

Week

Worst Park Avg.

Best Park Avg.

Worst Park ROI.

Best Park ROI

Total

This Week

94.48

102.03

-100%

100%

0%

Year to Date

99.31

105.72

217.24%

162.07%

189.66%

There are a few things I want to point out after updating the table. First, you might have noticed that the “Worst Park” ROI has taken a hit in the previous few weeks. I’m not sweating it though, because this is exactly what I’ve anticipated and touched on in the past. I don’t expect the worst park to generate as many “cashes” as the best park, however, when it does, I’m hoping thanks to low ownership levels that it brings us a larger ROI, just less frequently.

After seeing the best park score put up a 141, you might be shocked at how the best park average for the year could have possibly gone down this week. Well, surprisingly, I bit the bullet on one of the shorter slates, and put up a 46, crushing that average for the week and just slightly dropping the yearly total. Again, I’m not concerned about this. There is a lot of variance in baseball and as noted by the best park average this week, and the total for the year, it always seems to work itself out.

Here’s to next week!

Thoughts on Logan’s GPP experiment? Want to start an experiment of your own?

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